학술논문
Semantic Segmentation with a Sparse Convolutional Neural Network for Event Reconstruction in MicroBooNE
Document Type
Working Paper
Author
MicroBooNE collaboration; Abratenko, P.; Alrashed, M.; An, R.; Anthony, J.; Asaadi, J.; Ashkenazi, A.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Basque, V.; Bathe-Peters, L.; Rodrigues, O. Benevides; Berkman, S.; Bhanderi, A.; Bhat, A.; Bishai, M.; Blake, A.; Bolton, T.; Camilleri, L.; Caratelli, D.; Terrazas, I. Caro; Fernandez, R. Castillo; Cavanna, F.; Cerati, G.; Chen, Y.; Church, E.; Cianci, D.; Conrad, J. M.; Convery, M.; Cooper-Troendle, L.; Crespo-Anadon, J. I.; Del Tutto, M.; Dennis, S. R.; Devitt, D.; Diurba, R.; Dorrill, R.; Duffy, K.; Dytman, S.; Eberly, B.; Ereditato, A.; Evans, J. J.; Aguirre, G. A. Fiorentini; Fitzpatrick, R. S.; Fleming, B. T.; Foppiani, N.; Franco, D.; Furmanski, A. P.; Garcia-Gamez, D.; Gardiner, S.; Ge, G.; Gollapinni, S.; Goodwin, O.; Gramellini, E.; Green, P.; Greenlee, H.; Gu, W.; Guenette, R.; Guzowski, P.; Hagaman, L.; Hall, E.; Hamilton, P.; Hen, O.; Horton-Smith, G. A.; Hourlier, A.; Itay, R.; James, C.; de Vries, J. Jan; Ji, X.; Jiang, L.; Jo, J. H.; Johnson, R. A.; Jwa, Y. J.; Kamp, N.; Kaneshige, N.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; LaZur, R.; Lepetic, I.; Li, K.; Li, Y.; Littlejohn, B. R.; Louis, W. C.; Luo, X.; Marchionni, A.; Mariani, C.; Marsden, D.; Marshall, J.; Martin-Albo, J.; Caicedo, D. A. Martinez; Mason, K.; Mastbaum, A.; McConkey, N.; Meddage, V.; Mettler, T.; Miller, K.; Mills, J.; Mistry, K.; Mohayai, T.; Mogan, A.; Moon, J.; Mooney, M.; Moor, A. F.; Moore, C. D.; Lepin, L. Mora; Mousseau, J.; Murphy, M.; Naples, D.; Navrer-Agasson, A.; Neely, R. K.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papadopoulou, A.; Papavassiliou, V.; Pate, S. F.; Paudel, A.; Pavlovic, Z.; Piasetzky, E.; Ponce-Pinto, I.; Prince, S.; Qian, X.; Raaf, J. L.; Radeka, V.; Rafique, A.; Reggiani-Guzzo, M.; Ren, L.; Rochester, L.; Rondon, J. Rodriguez; Rogers, H. E.; Rosenberg, M.; Ross-Lonergan, M.; Russell, B.; Scanavini, G.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sharankova, R.; Sinclair, J.; Smith, A.; Snider, E. L.; Soderberg, M.; Soldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; Stancari, M.; John, J. St.; Strauss, T.; Sutton, K.; Sword-Fehlberg, S.; Szelc, A. M.; Tagg, N.; Tang, W.; Terao, K.; Thorpe, C.; Toups, M.; Tsai, Y. -T.; Uchida, M. A.; Usher, T.; Van De Pontseele, W.; Viren, B.; Weber, M.; Wei, H.; Williams, Z.; Wolbers, S.; Wongjirad, T.; Wospakrik, M.; Wu, W.; Yandel, E.; Yang, T.; Yarbrough, G.; Yates, L. E.; Zeller, G. P.; Zennamo, J.; Zhang, C.
Source
Phys. Rev. D 103, 052012 (2021)
Subject
Language
Abstract
We present the performance of a semantic segmentation network, SparseSSNet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. SparseSSNet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNE's $\nu_e$-appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are re-classified into two classes more relevant to the current analysis. The output of SparseSSNet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. The accuracy achieved on the test sample is $\geq 99\%$. For full neutrino interaction simulations, the time for processing one image is $\approx$ 0.5 sec, the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.